Overview

Dataset statistics

Number of variables21
Number of observations3256
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory489.8 KiB
Average record size in memory154.0 B

Variable types

Numeric9
Categorical10
Boolean2

Alerts

Case_Number has a high cardinality: 3256 distinct valuesHigh cardinality
Date has a high cardinality: 1642 distinct valuesHigh cardinality
Block has a high cardinality: 2761 distinct valuesHigh cardinality
IUCR has a high cardinality: 158 distinct valuesHigh cardinality
Description has a high cardinality: 146 distinct valuesHigh cardinality
Location_Description has a high cardinality: 73 distinct valuesHigh cardinality
Location has a high cardinality: 3092 distinct valuesHigh cardinality
ID is highly overall correlated with Primary_Type and 2 other fieldsHigh correlation
Beat is highly overall correlated with District and 6 other fieldsHigh correlation
District is highly overall correlated with Beat and 6 other fieldsHigh correlation
Ward is highly overall correlated with Beat and 4 other fieldsHigh correlation
Community_Area is highly overall correlated with Beat and 4 other fieldsHigh correlation
X_Coordinate is highly overall correlated with Beat and 4 other fieldsHigh correlation
Y_Coordinate is highly overall correlated with Beat and 6 other fieldsHigh correlation
Latitude is highly overall correlated with Beat and 6 other fieldsHigh correlation
Longitude is highly overall correlated with Beat and 4 other fieldsHigh correlation
Primary_Type is highly overall correlated with ID and 4 other fieldsHigh correlation
Arrest is highly overall correlated with Primary_Type and 1 other fieldsHigh correlation
Domestic is highly overall correlated with Primary_Type and 1 other fieldsHigh correlation
FBI_Code is highly overall correlated with ID and 4 other fieldsHigh correlation
Updated_On is highly overall correlated with ID and 2 other fieldsHigh correlation
Updated_On is highly imbalanced (96.3%)Imbalance
Case_Number is uniformly distributedUniform
Block is uniformly distributedUniform
Location is uniformly distributedUniform
ID has unique valuesUnique
Case_Number has unique valuesUnique

Reproduction

Analysis started2023-04-14 23:09:51.498769
Analysis finished2023-04-14 23:10:10.607133
Duration19.11 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3256
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7539492
Minimum4785
Maximum7588414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:10.774968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum4785
5-th percentile7583757.8
Q17584795.8
median7585953.5
Q37587392.2
95-th percentile7588218.2
Maximum7588414
Range7583629
Interquartile range (IQR)2596.5

Descriptive statistics

Standard deviation592440.28
Coefficient of variation (CV)0.078578275
Kurtosis158.04868
Mean7539492
Median Absolute Deviation (MAD)1336
Skewness-12.647158
Sum2.4548586 × 1010
Variance3.5098548 × 1011
MonotonicityNot monotonic
2023-04-14T23:10:11.043099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4785 1
 
< 0.1%
7587064 1
 
< 0.1%
7587051 1
 
< 0.1%
7587054 1
 
< 0.1%
7587055 1
 
< 0.1%
7587056 1
 
< 0.1%
7587057 1
 
< 0.1%
7587058 1
 
< 0.1%
7587059 1
 
< 0.1%
7587060 1
 
< 0.1%
Other values (3246) 3246
99.7%
ValueCountFrequency (%)
4785 1
< 0.1%
4786 1
< 0.1%
4787 1
< 0.1%
4788 1
< 0.1%
4789 1
< 0.1%
4790 1
< 0.1%
4791 1
< 0.1%
4792 1
< 0.1%
4793 1
< 0.1%
4794 1
< 0.1%
ValueCountFrequency (%)
7588414 1
< 0.1%
7588413 1
< 0.1%
7588412 1
< 0.1%
7588411 1
< 0.1%
7588408 1
< 0.1%
7588407 1
< 0.1%
7588406 1
< 0.1%
7588405 1
< 0.1%
7588404 1
< 0.1%
7588403 1
< 0.1%

Case_Number
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct3256
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
HP610824
 
1
HS390683
 
1
HS390635
 
1
HS390464
 
1
HS390588
 
1
Other values (3251)
3251 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters26048
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3256 ?
Unique (%)100.0%

Sample

1st rowHP610824
2nd rowHP616595
3rd rowHP616904
4th rowHP618616
5th rowHP619020

Common Values

ValueCountFrequency (%)
HP610824 1
 
< 0.1%
HS390683 1
 
< 0.1%
HS390635 1
 
< 0.1%
HS390464 1
 
< 0.1%
HS390588 1
 
< 0.1%
HS390494 1
 
< 0.1%
HS390658 1
 
< 0.1%
HS390682 1
 
< 0.1%
HS390674 1
 
< 0.1%
HS390646 1
 
< 0.1%
Other values (3246) 3246
99.7%

Length

2023-04-14T23:10:11.287657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hp610824 1
 
< 0.1%
hp623937 1
 
< 0.1%
hs387357 1
 
< 0.1%
hs368462 1
 
< 0.1%
hp616904 1
 
< 0.1%
hp618616 1
 
< 0.1%
hp619020 1
 
< 0.1%
hp620131 1
 
< 0.1%
hp620406 1
 
< 0.1%
hp622040 1
 
< 0.1%
Other values (3246) 3246
99.7%

Most occurring characters

ValueCountFrequency (%)
3 4220
16.2%
8 3397
13.0%
H 3256
12.5%
S 3223
12.4%
9 2788
10.7%
7 1591
 
6.1%
1 1550
 
6.0%
0 1469
 
5.6%
2 1253
 
4.8%
6 1151
 
4.4%
Other values (4) 2150
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19536
75.0%
Uppercase Letter 6512
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 4220
21.6%
8 3397
17.4%
9 2788
14.3%
7 1591
 
8.1%
1 1550
 
7.9%
0 1469
 
7.5%
2 1253
 
6.4%
6 1151
 
5.9%
4 1070
 
5.5%
5 1047
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
H 3256
50.0%
S 3223
49.5%
P 20
 
0.3%
R 13
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 19536
75.0%
Latin 6512
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 4220
21.6%
8 3397
17.4%
9 2788
14.3%
7 1591
 
8.1%
1 1550
 
7.9%
0 1469
 
7.5%
2 1253
 
6.4%
6 1151
 
5.9%
4 1070
 
5.5%
5 1047
 
5.4%
Latin
ValueCountFrequency (%)
H 3256
50.0%
S 3223
49.5%
P 20
 
0.3%
R 13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26048
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 4220
16.2%
8 3397
13.0%
H 3256
12.5%
S 3223
12.4%
9 2788
10.7%
7 1591
 
6.1%
1 1550
 
6.0%
0 1469
 
5.6%
2 1253
 
4.8%
6 1151
 
4.4%
Other values (4) 2150
8.3%

Date
Categorical

Distinct1642
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
06/30/2010 07:00:00 PM
 
25
06/30/2010 08:00:00 PM
 
22
07/01/2010 02:00:00 PM
 
21
07/01/2010 12:00:00 PM
 
20
06/30/2010 10:00:00 PM
 
20
Other values (1637)
3148 

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters71632
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1122 ?
Unique (%)34.5%

Sample

1st row10/07/2008 12:39:00 PM
2nd row10/09/2008 03:30:00 AM
3rd row10/09/2008 08:35:00 AM
4th row10/10/2008 02:33:00 AM
5th row10/10/2008 12:50:00 PM

Common Values

ValueCountFrequency (%)
06/30/2010 07:00:00 PM 25
 
0.8%
06/30/2010 08:00:00 PM 22
 
0.7%
07/01/2010 02:00:00 PM 21
 
0.6%
07/01/2010 12:00:00 PM 20
 
0.6%
06/30/2010 10:00:00 PM 20
 
0.6%
07/02/2010 10:00:00 PM 19
 
0.6%
07/02/2010 12:00:00 PM 18
 
0.6%
07/01/2010 01:00:00 PM 17
 
0.5%
07/02/2010 07:00:00 PM 17
 
0.5%
06/30/2010 09:00:00 PM 16
 
0.5%
Other values (1632) 3061
94.0%

Length

2023-04-14T23:10:11.497646image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pm 2117
21.7%
am 1139
 
11.7%
07/01/2010 889
 
9.1%
07/02/2010 740
 
7.6%
06/30/2010 593
 
6.1%
07/03/2010 328
 
3.4%
06/29/2010 134
 
1.4%
10:00:00 130
 
1.3%
12:00:00 105
 
1.1%
09:00:00 100
 
1.0%
Other values (630) 3493
35.8%

Most occurring characters

ValueCountFrequency (%)
0 24741
34.5%
/ 6512
 
9.1%
6512
 
9.1%
: 6512
 
9.1%
1 6412
 
9.0%
2 5557
 
7.8%
M 3256
 
4.5%
7 2405
 
3.4%
P 2117
 
3.0%
3 1982
 
2.8%
Other values (6) 5626
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45584
63.6%
Other Punctuation 13024
 
18.2%
Space Separator 6512
 
9.1%
Uppercase Letter 6512
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24741
54.3%
1 6412
 
14.1%
2 5557
 
12.2%
7 2405
 
5.3%
3 1982
 
4.3%
6 1562
 
3.4%
5 1204
 
2.6%
4 717
 
1.6%
9 532
 
1.2%
8 472
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
M 3256
50.0%
P 2117
32.5%
A 1139
 
17.5%
Other Punctuation
ValueCountFrequency (%)
/ 6512
50.0%
: 6512
50.0%
Space Separator
ValueCountFrequency (%)
6512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65120
90.9%
Latin 6512
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24741
38.0%
/ 6512
 
10.0%
6512
 
10.0%
: 6512
 
10.0%
1 6412
 
9.8%
2 5557
 
8.5%
7 2405
 
3.7%
3 1982
 
3.0%
6 1562
 
2.4%
5 1204
 
1.8%
Other values (3) 1721
 
2.6%
Latin
ValueCountFrequency (%)
M 3256
50.0%
P 2117
32.5%
A 1139
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24741
34.5%
/ 6512
 
9.1%
6512
 
9.1%
: 6512
 
9.1%
1 6412
 
9.0%
2 5557
 
7.8%
M 3256
 
4.5%
7 2405
 
3.4%
P 2117
 
3.0%
3 1982
 
2.8%
Other values (6) 5626
 
7.9%

Block
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct2761
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
0000X N STATE ST
 
11
001XX W 87TH ST
 
7
008XX N MICHIGAN AVE
 
7
038XX W NORTH AVE
 
5
063XX S DR MARTIN LUTHER KING JR DR
 
5
Other values (2756)
3221 

Length

Max length35
Median length25
Mean length18.507985
Min length14

Characters and Unicode

Total characters60262
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2363 ?
Unique (%)72.6%

Sample

1st row000XX E 75TH ST
2nd row048XX W POLK ST
3rd row030XX W MANN DR
4th row052XX W CHICAGO AVE
5th row026XX S HOMAN AVE

Common Values

ValueCountFrequency (%)
0000X N STATE ST 11
 
0.3%
001XX W 87TH ST 7
 
0.2%
008XX N MICHIGAN AVE 7
 
0.2%
038XX W NORTH AVE 5
 
0.2%
063XX S DR MARTIN LUTHER KING JR DR 5
 
0.2%
042XX S COTTAGE GROVE AVE 5
 
0.2%
088XX S BURLEY AVE 4
 
0.1%
0000X W 79TH ST 4
 
0.1%
033XX W FILLMORE ST 4
 
0.1%
025XX N NARRAGANSETT AVE 4
 
0.1%
Other values (2751) 3200
98.3%

Length

2023-04-14T23:10:11.718205image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ave 1734
 
13.0%
s 1324
 
9.9%
st 1145
 
8.6%
w 957
 
7.2%
n 760
 
5.7%
e 219
 
1.6%
dr 112
 
0.8%
0000x 101
 
0.8%
rd 92
 
0.7%
blvd 91
 
0.7%
Other values (771) 6782
50.9%

Most occurring characters

ValueCountFrequency (%)
10061
16.7%
X 6450
 
10.7%
0 4253
 
7.1%
E 4196
 
7.0%
A 3926
 
6.5%
S 3559
 
5.9%
T 2541
 
4.2%
N 2530
 
4.2%
V 2037
 
3.4%
R 1904
 
3.2%
Other values (27) 18805
31.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 39380
65.3%
Decimal Number 10821
 
18.0%
Space Separator 10061
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 6450
16.4%
E 4196
10.7%
A 3926
10.0%
S 3559
 
9.0%
T 2541
 
6.5%
N 2530
 
6.4%
V 2037
 
5.2%
R 1904
 
4.8%
L 1684
 
4.3%
O 1475
 
3.7%
Other values (16) 9078
23.1%
Decimal Number
ValueCountFrequency (%)
0 4253
39.3%
1 1263
 
11.7%
3 799
 
7.4%
2 780
 
7.2%
5 752
 
6.9%
6 685
 
6.3%
4 677
 
6.3%
7 647
 
6.0%
8 513
 
4.7%
9 452
 
4.2%
Space Separator
ValueCountFrequency (%)
10061
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39380
65.3%
Common 20882
34.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 6450
16.4%
E 4196
10.7%
A 3926
10.0%
S 3559
 
9.0%
T 2541
 
6.5%
N 2530
 
6.4%
V 2037
 
5.2%
R 1904
 
4.8%
L 1684
 
4.3%
O 1475
 
3.7%
Other values (16) 9078
23.1%
Common
ValueCountFrequency (%)
10061
48.2%
0 4253
20.4%
1 1263
 
6.0%
3 799
 
3.8%
2 780
 
3.7%
5 752
 
3.6%
6 685
 
3.3%
4 677
 
3.2%
7 647
 
3.1%
8 513
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10061
16.7%
X 6450
 
10.7%
0 4253
 
7.1%
E 4196
 
7.0%
A 3926
 
6.5%
S 3559
 
5.9%
T 2541
 
4.2%
N 2530
 
4.2%
V 2037
 
3.4%
R 1904
 
3.2%
Other values (27) 18805
31.2%

IUCR
Categorical

Distinct158
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
486
297 
820
228 
1811
 
203
460
 
203
810
 
176
Other values (153)
2149 

Length

Max length4
Median length3
Mean length3.4097052
Min length3

Characters and Unicode

Total characters11102
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)1.0%

Sample

1st row110
2nd row110
3rd row110
4th row110
5th row110

Common Values

ValueCountFrequency (%)
486 297
 
9.1%
820 228
 
7.0%
1811 203
 
6.2%
460 203
 
6.2%
810 176
 
5.4%
610 147
 
4.5%
1320 143
 
4.4%
1310 135
 
4.1%
560 131
 
4.0%
890 114
 
3.5%
Other values (148) 1479
45.4%

Length

2023-04-14T23:10:11.931918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
486 297
 
9.1%
820 228
 
7.0%
1811 203
 
6.2%
460 203
 
6.2%
810 176
 
5.4%
610 147
 
4.5%
1320 143
 
4.4%
1310 135
 
4.1%
560 131
 
4.0%
890 114
 
3.5%
Other values (148) 1479
45.4%

Most occurring characters

ValueCountFrequency (%)
0 2463
22.2%
1 2203
19.8%
8 1375
12.4%
2 1298
11.7%
6 1072
9.7%
4 836
 
7.5%
3 749
 
6.7%
5 524
 
4.7%
9 295
 
2.7%
7 143
 
1.3%
Other values (5) 144
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10958
98.7%
Uppercase Letter 144
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2463
22.5%
1 2203
20.1%
8 1375
12.5%
2 1298
11.8%
6 1072
9.8%
4 836
 
7.6%
3 749
 
6.8%
5 524
 
4.8%
9 295
 
2.7%
7 143
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
A 119
82.6%
P 16
 
11.1%
B 4
 
2.8%
R 3
 
2.1%
C 2
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 10958
98.7%
Latin 144
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2463
22.5%
1 2203
20.1%
8 1375
12.5%
2 1298
11.8%
6 1072
9.8%
4 836
 
7.6%
3 749
 
6.8%
5 524
 
4.8%
9 295
 
2.7%
7 143
 
1.3%
Latin
ValueCountFrequency (%)
A 119
82.6%
P 16
 
11.1%
B 4
 
2.8%
R 3
 
2.1%
C 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2463
22.2%
1 2203
19.8%
8 1375
12.4%
2 1298
11.7%
6 1072
9.7%
4 836
 
7.5%
3 749
 
6.7%
5 524
 
4.7%
9 295
 
2.7%
7 143
 
1.3%
Other values (5) 144
 
1.3%

Primary_Type
Categorical

Distinct24
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
THEFT
672 
BATTERY
605 
NARCOTICS
404 
CRIMINAL DAMAGE
304 
BURGLARY
241 
Other values (19)
1030 

Length

Max length32
Median length22
Mean length9.6464988
Min length5

Characters and Unicode

Total characters31409
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowHOMICIDE
2nd rowHOMICIDE
3rd rowHOMICIDE
4th rowHOMICIDE
5th rowHOMICIDE

Common Values

ValueCountFrequency (%)
THEFT 672
20.6%
BATTERY 605
18.6%
NARCOTICS 404
12.4%
CRIMINAL DAMAGE 304
9.3%
BURGLARY 241
 
7.4%
OTHER OFFENSE 204
 
6.3%
ASSAULT 185
 
5.7%
ROBBERY 143
 
4.4%
MOTOR VEHICLE THEFT 132
 
4.1%
DECEPTIVE PRACTICE 119
 
3.7%
Other values (14) 247
 
7.6%

Length

2023-04-14T23:10:12.160206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
theft 804
18.2%
battery 605
13.7%
narcotics 404
9.2%
criminal 382
8.7%
damage 304
 
6.9%
burglary 241
 
5.5%
offense 229
 
5.2%
other 204
 
4.6%
assault 192
 
4.4%
robbery 143
 
3.2%
Other values (25) 898
20.4%

Most occurring characters

ValueCountFrequency (%)
T 4219
13.4%
E 3508
 
11.2%
A 2977
 
9.5%
R 2775
 
8.8%
I 1900
 
6.0%
C 1802
 
5.7%
O 1498
 
4.8%
S 1319
 
4.2%
F 1286
 
4.1%
N 1213
 
3.9%
Other values (15) 8912
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 30259
96.3%
Space Separator 1150
 
3.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 4219
13.9%
E 3508
11.6%
A 2977
 
9.8%
R 2775
 
9.2%
I 1900
 
6.3%
C 1802
 
6.0%
O 1498
 
5.0%
S 1319
 
4.4%
F 1286
 
4.2%
N 1213
 
4.0%
Other values (14) 7762
25.7%
Space Separator
ValueCountFrequency (%)
1150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30259
96.3%
Common 1150
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 4219
13.9%
E 3508
11.6%
A 2977
 
9.8%
R 2775
 
9.2%
I 1900
 
6.3%
C 1802
 
6.0%
O 1498
 
5.0%
S 1319
 
4.4%
F 1286
 
4.2%
N 1213
 
4.0%
Other values (14) 7762
25.7%
Common
ValueCountFrequency (%)
1150
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31409
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 4219
13.4%
E 3508
 
11.2%
A 2977
 
9.5%
R 2775
 
8.8%
I 1900
 
6.0%
C 1802
 
5.7%
O 1498
 
4.8%
S 1319
 
4.2%
F 1286
 
4.1%
N 1213
 
3.9%
Other values (15) 8912
28.4%

Description
Categorical

Distinct146
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
SIMPLE
337 
DOMESTIC BATTERY SIMPLE
297 
$500 AND UNDER
228 
POSS: CANNABIS 30GMS OR LESS
203 
OVER $500
 
176
Other values (141)
2015 

Length

Max length47
Median length31
Mean length16.55344
Min length6

Characters and Unicode

Total characters53898
Distinct characters39
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.9%

Sample

1st rowFIRST DEGREE MURDER
2nd rowFIRST DEGREE MURDER
3rd rowFIRST DEGREE MURDER
4th rowFIRST DEGREE MURDER
5th rowFIRST DEGREE MURDER

Common Values

ValueCountFrequency (%)
SIMPLE 337
 
10.4%
DOMESTIC BATTERY SIMPLE 297
 
9.1%
$500 AND UNDER 228
 
7.0%
POSS: CANNABIS 30GMS OR LESS 203
 
6.2%
OVER $500 176
 
5.4%
TO VEHICLE 150
 
4.6%
FORCIBLE ENTRY 147
 
4.5%
TO PROPERTY 135
 
4.1%
FROM BUILDING 114
 
3.5%
AUTOMOBILE 110
 
3.4%
Other values (136) 1359
41.7%

Length

2023-04-14T23:10:12.401149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
simple 634
 
7.6%
500 404
 
4.9%
to 391
 
4.7%
poss 363
 
4.4%
domestic 314
 
3.8%
battery 314
 
3.8%
under 239
 
2.9%
entry 233
 
2.8%
or 231
 
2.8%
and 228
 
2.7%
Other values (217) 4968
59.7%

Most occurring characters

ValueCountFrequency (%)
E 5358
 
9.9%
5063
 
9.4%
T 3581
 
6.6%
S 3389
 
6.3%
O 3389
 
6.3%
R 3275
 
6.1%
A 3235
 
6.0%
I 3091
 
5.7%
N 3088
 
5.7%
L 2356
 
4.4%
Other values (29) 18073
33.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 45489
84.4%
Space Separator 5063
 
9.4%
Decimal Number 1768
 
3.3%
Other Punctuation 908
 
1.7%
Currency Symbol 436
 
0.8%
Dash Punctuation 104
 
0.2%
Open Punctuation 65
 
0.1%
Close Punctuation 65
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5358
11.8%
T 3581
 
7.9%
S 3389
 
7.5%
O 3389
 
7.5%
R 3275
 
7.2%
A 3235
 
7.1%
I 3091
 
6.8%
N 3088
 
6.8%
L 2356
 
5.2%
M 1903
 
4.2%
Other values (15) 12824
28.2%
Other Punctuation
ValueCountFrequency (%)
: 655
72.1%
/ 206
 
22.7%
, 30
 
3.3%
& 11
 
1.2%
. 6
 
0.7%
Decimal Number
ValueCountFrequency (%)
0 1102
62.3%
5 404
 
22.9%
3 248
 
14.0%
1 14
 
0.8%
Space Separator
ValueCountFrequency (%)
5063
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 436
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45489
84.4%
Common 8409
 
15.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5358
11.8%
T 3581
 
7.9%
S 3389
 
7.5%
O 3389
 
7.5%
R 3275
 
7.2%
A 3235
 
7.1%
I 3091
 
6.8%
N 3088
 
6.8%
L 2356
 
5.2%
M 1903
 
4.2%
Other values (15) 12824
28.2%
Common
ValueCountFrequency (%)
5063
60.2%
0 1102
 
13.1%
: 655
 
7.8%
$ 436
 
5.2%
5 404
 
4.8%
3 248
 
2.9%
/ 206
 
2.4%
- 104
 
1.2%
( 65
 
0.8%
) 65
 
0.8%
Other values (4) 61
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 5358
 
9.9%
5063
 
9.4%
T 3581
 
6.6%
S 3389
 
6.3%
O 3389
 
6.3%
R 3275
 
6.1%
A 3235
 
6.0%
I 3091
 
5.7%
N 3088
 
5.7%
L 2356
 
4.4%
Other values (29) 18073
33.5%
Distinct73
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
STREET
799 
RESIDENCE
524 
SIDEWALK
427 
APARTMENT
356 
OTHER
108 
Other values (68)
1042 

Length

Max length46
Median length42
Mean length10.90387
Min length4

Characters and Unicode

Total characters35503
Distinct characters31
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.3%

Sample

1st rowALLEY
2nd rowSTREET
3rd rowPARK PROPERTY
4th rowRESTAURANT
5th rowGARAGE

Common Values

ValueCountFrequency (%)
STREET 799
24.5%
RESIDENCE 524
16.1%
SIDEWALK 427
13.1%
APARTMENT 356
10.9%
OTHER 108
 
3.3%
ALLEY 91
 
2.8%
PARKING LOT/GARAGE(NON.RESID.) 77
 
2.4%
RESIDENCE-GARAGE 74
 
2.3%
PARK PROPERTY 62
 
1.9%
RESIDENTIAL YARD (FRONT/BACK) 61
 
1.9%
Other values (63) 677
20.8%

Length

2023-04-14T23:10:12.637284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street 799
18.0%
residence 581
 
13.1%
sidewalk 427
 
9.6%
apartment 373
 
8.4%
store 169
 
3.8%
other 118
 
2.7%
parking 117
 
2.6%
alley 91
 
2.1%
lot/garage(non.resid 77
 
1.7%
residence-garage 74
 
1.7%
Other values (105) 1607
36.3%

Most occurring characters

ValueCountFrequency (%)
E 5958
16.8%
T 3589
10.1%
R 3562
10.0%
A 2919
 
8.2%
S 2556
 
7.2%
I 1975
 
5.6%
N 1961
 
5.5%
L 1521
 
4.3%
D 1504
 
4.2%
C 1367
 
3.9%
Other values (21) 8591
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 33313
93.8%
Space Separator 1177
 
3.3%
Other Punctuation 565
 
1.6%
Open Punctuation 150
 
0.4%
Close Punctuation 150
 
0.4%
Dash Punctuation 148
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 5958
17.9%
T 3589
10.8%
R 3562
10.7%
A 2919
8.8%
S 2556
 
7.7%
I 1975
 
5.9%
N 1961
 
5.9%
L 1521
 
4.6%
D 1504
 
4.5%
C 1367
 
4.1%
Other values (14) 6401
19.2%
Other Punctuation
ValueCountFrequency (%)
/ 351
62.1%
. 154
27.3%
, 60
 
10.6%
Space Separator
ValueCountFrequency (%)
1177
100.0%
Open Punctuation
ValueCountFrequency (%)
( 150
100.0%
Close Punctuation
ValueCountFrequency (%)
) 150
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 33313
93.8%
Common 2190
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 5958
17.9%
T 3589
10.8%
R 3562
10.7%
A 2919
8.8%
S 2556
 
7.7%
I 1975
 
5.9%
N 1961
 
5.9%
L 1521
 
4.6%
D 1504
 
4.5%
C 1367
 
4.1%
Other values (14) 6401
19.2%
Common
ValueCountFrequency (%)
1177
53.7%
/ 351
 
16.0%
. 154
 
7.0%
( 150
 
6.8%
) 150
 
6.8%
- 148
 
6.8%
, 60
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35503
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 5958
16.8%
T 3589
10.1%
R 3562
10.0%
A 2919
 
8.2%
S 2556
 
7.2%
I 1975
 
5.6%
N 1961
 
5.5%
L 1521
 
4.3%
D 1504
 
4.2%
C 1367
 
3.9%
Other values (21) 8591
24.2%

Arrest
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
False
2351 
True
905 
ValueCountFrequency (%)
False 2351
72.2%
True 905
 
27.8%
2023-04-14T23:10:12.871703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Domestic
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
False
2794 
True
462 
ValueCountFrequency (%)
False 2794
85.8%
True 462
 
14.2%
2023-04-14T23:10:13.041272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Beat
Real number (ℝ)

Distinct284
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1207.1625
Minimum111
Maximum2535
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:13.204022image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile213
Q1622
median1111
Q31813
95-th percentile2512.25
Maximum2535
Range2424
Interquartile range (IQR)1191

Descriptive statistics

Standard deviation714.15851
Coefficient of variation (CV)0.59160099
Kurtosis-1.0535109
Mean1207.1625
Median Absolute Deviation (MAD)587
Skewness0.34337858
Sum3930521
Variance510022.38
MonotonicityNot monotonic
2023-04-14T23:10:13.478723image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
624 29
 
0.9%
1834 27
 
0.8%
823 27
 
0.8%
2533 26
 
0.8%
1112 25
 
0.8%
2535 25
 
0.8%
431 24
 
0.7%
424 24
 
0.7%
123 24
 
0.7%
1021 24
 
0.7%
Other values (274) 3001
92.2%
ValueCountFrequency (%)
111 12
0.4%
112 8
 
0.2%
113 12
0.4%
122 17
0.5%
123 24
0.7%
124 19
0.6%
131 14
0.4%
132 21
0.6%
133 6
 
0.2%
134 8
 
0.2%
ValueCountFrequency (%)
2535 25
0.8%
2534 11
0.3%
2533 26
0.8%
2532 18
0.6%
2531 14
0.4%
2525 4
 
0.1%
2524 13
0.4%
2523 6
 
0.2%
2522 15
0.5%
2521 11
0.3%

District
Real number (ℝ)

Distinct22
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.347666
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:13.694052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median10
Q317
95-th percentile25
Maximum25
Range24
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.0109513
Coefficient of variation (CV)0.61783202
Kurtosis-0.92697655
Mean11.347666
Median Absolute Deviation (MAD)5
Skewness0.4092252
Sum36948
Variance49.153438
MonotonicityNot monotonic
2023-04-14T23:10:13.920944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
8 225
 
6.9%
6 193
 
5.9%
25 188
 
5.8%
4 185
 
5.7%
7 183
 
5.6%
11 183
 
5.6%
12 160
 
4.9%
5 160
 
4.9%
19 158
 
4.9%
9 157
 
4.8%
Other values (12) 1464
45.0%
ValueCountFrequency (%)
1 145
4.5%
2 145
4.5%
3 155
4.8%
4 185
5.7%
5 160
4.9%
6 193
5.9%
7 183
5.6%
8 225
6.9%
9 157
4.8%
10 138
4.2%
ValueCountFrequency (%)
25 188
5.8%
24 85
2.6%
22 129
4.0%
20 57
 
1.8%
19 158
4.9%
18 147
4.5%
17 115
3.5%
16 109
3.3%
15 132
4.1%
14 107
3.3%

Ward
Real number (ℝ)

Distinct50
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.152334
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:14.154948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median23
Q334
95-th percentile46
Maximum50
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.980681
Coefficient of variation (CV)0.6038562
Kurtosis-1.116055
Mean23.152334
Median Absolute Deviation (MAD)12
Skewness0.13157775
Sum75384
Variance195.45943
MonotonicityNot monotonic
2023-04-14T23:10:14.424328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 166
 
5.1%
2 133
 
4.1%
28 128
 
3.9%
24 124
 
3.8%
21 120
 
3.7%
34 118
 
3.6%
6 115
 
3.5%
27 112
 
3.4%
15 99
 
3.0%
20 99
 
3.0%
Other values (40) 2042
62.7%
ValueCountFrequency (%)
1 52
 
1.6%
2 133
4.1%
3 92
2.8%
4 59
1.8%
5 69
2.1%
6 115
3.5%
7 79
2.4%
8 85
2.6%
9 80
2.5%
10 70
2.1%
ValueCountFrequency (%)
50 38
 
1.2%
49 33
 
1.0%
48 39
 
1.2%
47 35
 
1.1%
46 40
 
1.2%
45 37
 
1.1%
44 56
 
1.7%
43 51
 
1.6%
42 166
5.1%
41 27
 
0.8%

Community_Area
Real number (ℝ)

Distinct77
Distinct (%)2.4%
Missing6
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.686769
Minimum1
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:14.681962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q123
median32
Q357
95-th percentile71
Maximum77
Range76
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.648008
Coefficient of variation (CV)0.5744193
Kurtosis-1.1429327
Mean37.686769
Median Absolute Deviation (MAD)17
Skewness0.16912505
Sum122482
Variance468.63624
MonotonicityNot monotonic
2023-04-14T23:10:14.953343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 200
 
6.1%
8 120
 
3.7%
23 110
 
3.4%
29 100
 
3.1%
32 100
 
3.1%
28 96
 
2.9%
49 96
 
2.9%
67 95
 
2.9%
24 94
 
2.9%
43 92
 
2.8%
Other values (67) 2147
65.9%
ValueCountFrequency (%)
1 36
 
1.1%
2 39
 
1.2%
3 43
 
1.3%
4 22
 
0.7%
5 18
 
0.6%
6 78
2.4%
7 54
1.7%
8 120
3.7%
9 3
 
0.1%
10 18
 
0.6%
ValueCountFrequency (%)
77 37
1.1%
76 10
 
0.3%
75 30
 
0.9%
74 6
 
0.2%
73 49
1.5%
72 14
 
0.4%
71 87
2.7%
70 26
 
0.8%
69 83
2.5%
68 80
2.5%

FBI_Code
Categorical

Distinct23
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
6
672 
08B
517 
18
383 
26
322 
14
304 
Other values (18)
1058 

Length

Max length3
Median length2
Mean length1.8820639
Min length1

Characters and Unicode

Total characters6128
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01A
2nd row01A
3rd row01A
4th row01A
5th row01A

Common Values

ValueCountFrequency (%)
6 672
20.6%
08B 517
15.9%
18 383
11.8%
26 322
9.9%
14 304
9.3%
5 241
 
7.4%
08A 145
 
4.5%
3 143
 
4.4%
7 132
 
4.1%
11 106
 
3.3%
Other values (13) 291
8.9%

Length

2023-04-14T23:10:15.223482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6 672
20.6%
08b 517
15.9%
18 383
11.8%
26 322
9.9%
14 304
9.3%
5 241
 
7.4%
08a 145
 
4.5%
3 143
 
4.4%
7 132
 
4.1%
11 106
 
3.3%
Other values (13) 291
8.9%

Most occurring characters

ValueCountFrequency (%)
8 1045
17.1%
6 1017
16.6%
1 1006
16.4%
0 830
13.5%
B 605
9.9%
4 464
7.6%
2 384
 
6.3%
5 265
 
4.3%
A 208
 
3.4%
3 143
 
2.3%
Other values (2) 161
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5315
86.7%
Uppercase Letter 813
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 1045
19.7%
6 1017
19.1%
1 1006
18.9%
0 830
15.6%
4 464
8.7%
2 384
 
7.2%
5 265
 
5.0%
3 143
 
2.7%
7 142
 
2.7%
9 19
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
B 605
74.4%
A 208
 
25.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5315
86.7%
Latin 813
 
13.3%

Most frequent character per script

Common
ValueCountFrequency (%)
8 1045
19.7%
6 1017
19.1%
1 1006
18.9%
0 830
15.6%
4 464
8.7%
2 384
 
7.2%
5 265
 
5.0%
3 143
 
2.7%
7 142
 
2.7%
9 19
 
0.4%
Latin
ValueCountFrequency (%)
B 605
74.4%
A 208
 
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 1045
17.1%
6 1017
16.6%
1 1006
16.4%
0 830
13.5%
B 605
9.9%
4 464
7.6%
2 384
 
6.3%
5 265
 
4.3%
A 208
 
3.4%
3 143
 
2.3%
Other values (2) 161
 
2.6%

X_Coordinate
Real number (ℝ)

Distinct2993
Distinct (%)92.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1164892
Minimum1100317
Maximum1204791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:15.490983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1100317
5-th percentile1138501.6
Q11153059.5
median1166546
Q31176446.5
95-th percentile1191318.3
Maximum1204791
Range104474
Interquartile range (IQR)23387

Descriptive statistics

Standard deviation16023.845
Coefficient of variation (CV)0.013755648
Kurtosis-0.16434701
Mean1164892
Median Absolute Deviation (MAD)11260
Skewness-0.209957
Sum3.7917235 × 109
Variance2.567636 × 108
MonotonicityNot monotonic
2023-04-14T23:10:15.761967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1176363 7
 
0.2%
1177338 6
 
0.2%
1150674 5
 
0.2%
1149512 4
 
0.1%
1199204 4
 
0.1%
1151145 4
 
0.1%
1143016 4
 
0.1%
1177483 4
 
0.1%
1182317 4
 
0.1%
1154228 4
 
0.1%
Other values (2983) 3209
98.6%
ValueCountFrequency (%)
1100317 1
< 0.1%
1100726 1
< 0.1%
1101667 2
0.1%
1106851 2
0.1%
1116632 1
< 0.1%
1116977 2
0.1%
1117558 1
< 0.1%
1120736 1
< 0.1%
1121841 1
< 0.1%
1122843 1
< 0.1%
ValueCountFrequency (%)
1204791 1
< 0.1%
1204456 1
< 0.1%
1204230 1
< 0.1%
1203829 1
< 0.1%
1202467 1
< 0.1%
1202212 1
< 0.1%
1202162 1
< 0.1%
1201989 1
< 0.1%
1201888 1
< 0.1%
1201815 1
< 0.1%

Y_Coordinate
Real number (ℝ)

Distinct3009
Distinct (%)92.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1885049.5
Minimum1814464
Maximum1951318
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:16.011779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1814464
5-th percentile1832766.1
Q11858279.5
median1890302
Q31909020
95-th percentile1934415.7
Maximum1951318
Range136854
Interquartile range (IQR)50740.5

Descriptive statistics

Standard deviation31932.542
Coefficient of variation (CV)0.016939896
Kurtosis-1.0165383
Mean1885049.5
Median Absolute Deviation (MAD)26644
Skewness-0.071674517
Sum6.135836 × 109
Variance1.0196872 × 109
MonotonicityNot monotonic
2023-04-14T23:10:16.270810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1900547 6
 
0.2%
1910363 5
 
0.2%
1906181 5
 
0.2%
1847271 5
 
0.2%
1900524 4
 
0.1%
1900768 4
 
0.1%
1852629 4
 
0.1%
1901446 4
 
0.1%
1895173 4
 
0.1%
1900577 4
 
0.1%
Other values (2999) 3210
98.6%
ValueCountFrequency (%)
1814464 1
< 0.1%
1814793 1
< 0.1%
1815106 1
< 0.1%
1817033 1
< 0.1%
1817128 1
< 0.1%
1817541 1
< 0.1%
1817559 1
< 0.1%
1817578 1
< 0.1%
1817699 1
< 0.1%
1818324 1
< 0.1%
ValueCountFrequency (%)
1951318 1
< 0.1%
1951183 1
< 0.1%
1951001 2
0.1%
1950609 1
< 0.1%
1950574 1
< 0.1%
1950521 1
< 0.1%
1950516 1
< 0.1%
1950357 1
< 0.1%
1950346 2
0.1%
1950211 1
< 0.1%

Updated_On
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.6 KiB
02/04/2016 06:33:39 AM
3235 
08/17/2015 03:03:40 PM
 
20
04/15/2016 11:16:05 AM
 
1

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters71632
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row08/17/2015 03:03:40 PM
2nd row08/17/2015 03:03:40 PM
3rd row08/17/2015 03:03:40 PM
4th row08/17/2015 03:03:40 PM
5th row08/17/2015 03:03:40 PM

Common Values

ValueCountFrequency (%)
02/04/2016 06:33:39 AM 3235
99.4%
08/17/2015 03:03:40 PM 20
 
0.6%
04/15/2016 11:16:05 AM 1
 
< 0.1%

Length

2023-04-14T23:10:16.512611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-14T23:10:16.744624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
am 3236
33.1%
02/04/2016 3235
33.1%
06:33:39 3235
33.1%
08/17/2015 20
 
0.2%
03:03:40 20
 
0.2%
pm 20
 
0.2%
04/15/2016 1
 
< 0.1%
11:16:05 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13043
18.2%
3 9745
13.6%
/ 6512
9.1%
6512
9.1%
: 6512
9.1%
2 6491
9.1%
6 6472
9.0%
1 3280
 
4.6%
4 3256
 
4.5%
M 3256
 
4.5%
Other values (6) 6553
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45584
63.6%
Other Punctuation 13024
 
18.2%
Space Separator 6512
 
9.1%
Uppercase Letter 6512
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13043
28.6%
3 9745
21.4%
2 6491
14.2%
6 6472
14.2%
1 3280
 
7.2%
4 3256
 
7.1%
9 3235
 
7.1%
5 22
 
< 0.1%
8 20
 
< 0.1%
7 20
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
M 3256
50.0%
A 3236
49.7%
P 20
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 6512
50.0%
: 6512
50.0%
Space Separator
ValueCountFrequency (%)
6512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65120
90.9%
Latin 6512
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13043
20.0%
3 9745
15.0%
/ 6512
10.0%
6512
10.0%
: 6512
10.0%
2 6491
10.0%
6 6472
9.9%
1 3280
 
5.0%
4 3256
 
5.0%
9 3235
 
5.0%
Other values (3) 62
 
0.1%
Latin
ValueCountFrequency (%)
M 3256
50.0%
A 3236
49.7%
P 20
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13043
18.2%
3 9745
13.6%
/ 6512
9.1%
6512
9.1%
: 6512
9.1%
2 6491
9.1%
6 6472
9.0%
1 3280
 
4.6%
4 3256
 
4.5%
M 3256
 
4.5%
Other values (6) 6553
9.1%

Latitude
Real number (ℝ)

Distinct3092
Distinct (%)95.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.840166
Minimum41.645647
Maximum42.022063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.6 KiB
2023-04-14T23:10:16.922834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum41.645647
5-th percentile41.696217
Q141.766273
median41.854771
Q341.906313
95-th percentile41.97632
Maximum42.022063
Range0.37641532
Interquartile range (IQR)0.14003986

Descriptive statistics

Standard deviation0.087814721
Coefficient of variation (CV)0.0020988138
Kurtosis-1.0185297
Mean41.840166
Median Absolute Deviation (MAD)0.073275188
Skewness-0.072593198
Sum136189.74
Variance0.0077114251
MonotonicityNot monotonic
2023-04-14T23:10:17.189885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.89789513 5
 
0.2%
41.90993405 5
 
0.2%
41.8824572 5
 
0.2%
41.88548754 4
 
0.1%
41.73625535 4
 
0.1%
41.75094076 4
 
0.1%
41.86818094 4
 
0.1%
41.88375061 4
 
0.1%
41.78195413 3
 
0.1%
41.87834366 3
 
0.1%
Other values (3082) 3214
98.7%
ValueCountFrequency (%)
41.64564736 1
< 0.1%
41.6470384 1
< 0.1%
41.64754054 1
< 0.1%
41.65268812 1
< 0.1%
41.6530237 1
< 0.1%
41.65459014 1
< 0.1%
41.65464613 1
< 0.1%
41.65465775 1
< 0.1%
41.65501226 1
< 0.1%
41.6566536 1
< 0.1%
ValueCountFrequency (%)
42.02206269 1
< 0.1%
42.02168201 1
< 0.1%
42.02117868 1
< 0.1%
42.02117847 1
< 0.1%
42.02010017 1
< 0.1%
42.0199755 1
< 0.1%
42.01984471 1
< 0.1%
42.01981196 1
< 0.1%
42.01947541 1
< 0.1%
42.01938122 1
< 0.1%

Longitude
Real number (ℝ)

Distinct3092
Distinct (%)95.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-87.670452
Minimum-87.906463
Maximum-87.525794
Zeros0
Zeros (%)0.0%
Negative3255
Negative (%)> 99.9%
Memory size25.6 KiB
2023-04-14T23:10:17.453063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-87.906463
5-th percentile-87.766656
Q1-87.713476
median-87.664206
Q3-87.627851
95-th percentile-87.574428
Maximum-87.525794
Range0.38066877
Interquartile range (IQR)0.085625423

Descriptive statistics

Standard deviation0.058303872
Coefficient of variation (CV)-0.00066503447
Kurtosis-0.15728514
Mean-87.670452
Median Absolute Deviation (MAD)0.04079073
Skewness-0.220161
Sum-285367.32
Variance0.0033993415
MonotonicityNot monotonic
2023-04-14T23:10:18.061817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.62409661 5
 
0.2%
-87.72192157 5
 
0.2%
-87.62784778 5
 
0.2%
-87.72642205 4
 
0.1%
-87.62820806 4
 
0.1%
-87.62518522 4
 
0.1%
-87.70927139 4
 
0.1%
-87.75029378 4
 
0.1%
-87.68371315 3
 
0.1%
-87.61986606 3
 
0.1%
Other values (3082) 3214
98.7%
ValueCountFrequency (%)
-87.90646315 1
< 0.1%
-87.90497627 1
< 0.1%
-87.9015144 2
0.1%
-87.88240413 2
0.1%
-87.84646896 1
< 0.1%
-87.84521442 2
0.1%
-87.8430843 1
< 0.1%
-87.83161567 1
< 0.1%
-87.82751409 1
< 0.1%
-87.82358953 1
< 0.1%
ValueCountFrequency (%)
-87.52579439 1
< 0.1%
-87.52699812 1
< 0.1%
-87.52776812 1
< 0.1%
-87.52924215 1
< 0.1%
-87.53407165 1
< 0.1%
-87.53528171 1
< 0.1%
-87.53528375 1
< 0.1%
-87.53597409 1
< 0.1%
-87.53648659 1
< 0.1%
-87.5364904 1
< 0.1%

Location
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3092
Distinct (%)95.0%
Missing1
Missing (%)< 0.1%
Memory size25.6 KiB
(41.897895128, -87.624096605)
 
5
(41.909934052, -87.72192157)
 
5
(41.882457198, -87.627847776)
 
5
(41.885487535, -87.726422045)
 
4
(41.73625535, -87.62820806)
 
4
Other values (3087)
3232 

Length

Max length29
Median length29
Mean length28.774808
Min length26

Characters and Unicode

Total characters93662
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2958 ?
Unique (%)90.9%

Sample

1st row(41.758275857, -87.622451031)
2nd row(41.87025207, -87.746069362)
3rd row(41.770990476, -87.698901469)
4th row(41.894916924, -87.757358147)
5th row(41.843826272, -87.709893465)

Common Values

ValueCountFrequency (%)
(41.897895128, -87.624096605) 5
 
0.2%
(41.909934052, -87.72192157) 5
 
0.2%
(41.882457198, -87.627847776) 5
 
0.2%
(41.885487535, -87.726422045) 4
 
0.1%
(41.73625535, -87.62820806) 4
 
0.1%
(41.750940757, -87.625185222) 4
 
0.1%
(41.868180939, -87.709271389) 4
 
0.1%
(41.883750613, -87.750293777) 4
 
0.1%
(41.781954133, -87.683713146) 3
 
0.1%
(41.878343664, -87.619866061) 3
 
0.1%
Other values (3082) 3214
98.7%

Length

2023-04-14T23:10:18.318431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41.897895128 5
 
0.1%
41.909934052 5
 
0.1%
87.72192157 5
 
0.1%
41.882457198 5
 
0.1%
87.627847776 5
 
0.1%
87.624096605 5
 
0.1%
87.625185222 4
 
0.1%
87.750293777 4
 
0.1%
41.883750613 4
 
0.1%
87.709271389 4
 
0.1%
Other values (6174) 6464
99.3%

Most occurring characters

ValueCountFrequency (%)
7 10466
11.2%
8 9586
10.2%
1 8313
 
8.9%
4 8295
 
8.9%
6 7345
 
7.8%
. 6510
 
7.0%
9 6101
 
6.5%
5 5611
 
6.0%
2 5334
 
5.7%
3 5186
 
5.5%
Other values (6) 20915
22.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70877
75.7%
Other Punctuation 9765
 
10.4%
Open Punctuation 3255
 
3.5%
Space Separator 3255
 
3.5%
Dash Punctuation 3255
 
3.5%
Close Punctuation 3255
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 10466
14.8%
8 9586
13.5%
1 8313
11.7%
4 8295
11.7%
6 7345
10.4%
9 6101
8.6%
5 5611
7.9%
2 5334
7.5%
3 5186
7.3%
0 4640
6.5%
Other Punctuation
ValueCountFrequency (%)
. 6510
66.7%
, 3255
33.3%
Open Punctuation
ValueCountFrequency (%)
( 3255
100.0%
Space Separator
ValueCountFrequency (%)
3255
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3255
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3255
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93662
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 10466
11.2%
8 9586
10.2%
1 8313
 
8.9%
4 8295
 
8.9%
6 7345
 
7.8%
. 6510
 
7.0%
9 6101
 
6.5%
5 5611
 
6.0%
2 5334
 
5.7%
3 5186
 
5.5%
Other values (6) 20915
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 10466
11.2%
8 9586
10.2%
1 8313
 
8.9%
4 8295
 
8.9%
6 7345
 
7.8%
. 6510
 
7.0%
9 6101
 
6.5%
5 5611
 
6.0%
2 5334
 
5.7%
3 5186
 
5.5%
Other values (6) 20915
22.3%

Interactions

2023-04-14T23:10:07.613610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:52.943878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:54.709588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:56.436339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:58.156164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:59.876343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:01.698907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:03.453266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:05.819163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:07.820019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:53.147248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:54.906333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:56.631272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:58.351389image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:00.082849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:01.898584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:03.657856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:06.025168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:08.010530image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:53.332743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:55.086331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:56.814802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:58.535130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:00.277559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:02.086652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:03.845084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:06.220720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:08.203495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:53.521993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:55.268026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:56.993909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:58.717655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:00.469097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:02.272534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:04.037146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:06.410322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:08.395023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:53.708539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:55.451495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:57.177315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:58.899081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:00.662510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:02.459186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:04.225710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:06.599311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:08.607083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:53.913731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:55.654157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:57.376244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:59.096583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:00.869339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:02.662041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:05.016079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:06.806027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:08.803728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:54.104572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:55.840074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:57.562916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:59.286604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:01.069557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:02.850585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:05.208606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:07.001954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:09.007814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:54.303806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:56.034919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:57.758264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:59.478854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:01.275374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:03.047832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:05.408195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:07.203726image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:09.214855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:54.505262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:56.233385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:57.957460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:09:59.677705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:01.483940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:03.252515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:05.614278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-14T23:10:07.406652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-14T23:10:18.506072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
IDBeatDistrictWardCommunity_AreaX_CoordinateY_CoordinateLatitudeLongitudePrimary_TypeLocation_DescriptionArrestDomesticFBI_CodeUpdated_On
ID1.000-0.012-0.0090.021-0.0140.045-0.002-0.0020.0450.9970.4880.0300.0000.9971.000
Beat-0.0121.0000.9260.604-0.541-0.5460.6410.642-0.5400.0860.1120.1260.0830.0800.023
District-0.0090.9261.0000.670-0.539-0.6260.6490.650-0.6210.0890.1090.1320.0880.0840.028
Ward0.0210.6040.6701.000-0.559-0.4410.6350.635-0.4350.0950.1250.1020.0830.0900.009
Community_Area-0.014-0.541-0.539-0.5591.0000.342-0.819-0.8190.3310.0890.1250.1300.1120.0830.009
X_Coordinate0.045-0.546-0.626-0.4410.3421.000-0.559-0.5621.0000.0490.2800.0820.0330.0500.000
Y_Coordinate-0.0020.6410.6490.635-0.819-0.5591.0001.000-0.5460.0720.1060.1010.1050.0710.043
Latitude-0.0020.6420.6500.635-0.819-0.5621.0001.000-0.5490.0720.1060.0920.1070.0710.044
Longitude0.045-0.540-0.621-0.4350.3311.000-0.546-0.5491.0000.0490.2800.0830.0320.0490.000
Primary_Type0.9970.0860.0890.0950.0890.0490.0720.0720.0491.0000.2040.7120.5260.8990.705
Location_Description0.4880.1120.1090.1250.1250.2800.1060.1060.2800.2041.0000.3380.3600.2090.330
Arrest0.0300.1260.1320.1020.1300.0820.1010.0920.0830.7120.3381.0000.0840.6850.041
Domestic0.0000.0830.0880.0830.1120.0330.1050.1070.0320.5260.3600.0841.0000.5340.000
FBI_Code0.9970.0800.0840.0900.0830.0500.0710.0710.0490.8990.2090.6850.5341.0000.706
Updated_On1.0000.0230.0280.0090.0090.0000.0430.0440.0000.7050.3300.0410.0000.7061.000

Missing values

2023-04-14T23:10:09.569692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-14T23:10:10.099768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-14T23:10:10.478976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDCase_NumberDateBlockIUCRPrimary_TypeDescriptionLocation_DescriptionArrestDomesticBeatDistrictWardCommunity_AreaFBI_CodeX_CoordinateY_CoordinateUpdated_OnLatitudeLongitudeLocation
04785HP61082410/07/2008 12:39:00 PM000XX E 75TH ST110HOMICIDEFIRST DEGREE MURDERALLEYTrueFalse3233.06.069.001A1178207.01855308.008/17/2015 03:03:40 PM41.758276-87.622451(41.758275857, -87.622451031)
14786HP61659510/09/2008 03:30:00 AM048XX W POLK ST110HOMICIDEFIRST DEGREE MURDERSTREETTrueFalse153315.024.025.001A1144200.01895857.008/17/2015 03:03:40 PM41.870252-87.746069(41.87025207, -87.746069362)
24787HP61690410/09/2008 08:35:00 AM030XX W MANN DR110HOMICIDEFIRST DEGREE MURDERPARK PROPERTYFalseFalse8318.018.066.001A1157314.01859778.008/17/2015 03:03:40 PM41.770990-87.698901(41.770990476, -87.698901469)
34788HP61861610/10/2008 02:33:00 AM052XX W CHICAGO AVE110HOMICIDEFIRST DEGREE MURDERRESTAURANTFalseFalse152415.037.025.001A1141065.01904824.008/17/2015 03:03:40 PM41.894917-87.757358(41.894916924, -87.757358147)
44789HP61902010/10/2008 12:50:00 PM026XX S HOMAN AVE110HOMICIDEFIRST DEGREE MURDERGARAGEFalseTrue103210.022.030.001A1154123.01886297.008/17/2015 03:03:40 PM41.843826-87.709893(41.843826272, -87.709893465)
54790HP62013110/10/2008 08:32:00 PM015XX W 14TH ST110HOMICIDEFIRST DEGREE MURDERSTREETTrueTrue123112.02.028.001A1166321.01893492.008/17/2015 03:03:40 PM41.863318-87.664924(41.863318307, -87.664923682)
64791HP62040610/11/2008 12:55:00 AM020XX W 23RD ST110HOMICIDEFIRST DEGREE MURDERSTREETTrueFalse103410.025.031.001A1163192.01888742.008/17/2015 03:03:40 PM41.850350-87.676543(41.850350125, -87.676543351)
74792HP62204010/11/2008 10:25:00 PM069XX S PAXTON AVE110HOMICIDEFIRST DEGREE MURDERSTREETFalseFalse3313.05.043.001A1192078.01859337.008/17/2015 03:03:40 PM41.769006-87.571485(41.769005966, -87.571485086)
84793HP62216410/11/2008 10:00:00 PM060XX S SAWYER AVE110HOMICIDEFIRST DEGREE MURDERPORCHFalseFalse8238.016.066.001A1155755.01864457.008/17/2015 03:03:40 PM41.783862-87.704491(41.783861768, -87.704490821)
94794HP62218910/12/2008 05:47:00 AM024XX W 51ST ST110HOMICIDEFIRST DEGREE MURDERALLEYFalseFalse9119.014.063.001A1160775.01870785.008/17/2015 03:03:40 PM41.801124-87.685911(41.801124416, -87.685910818)
IDCase_NumberDateBlockIUCRPrimary_TypeDescriptionLocation_DescriptionArrestDomesticBeatDistrictWardCommunity_AreaFBI_CodeX_CoordinateY_CoordinateUpdated_OnLatitudeLongitudeLocation
32467588403HS39245007/03/2010 05:21:00 PM037XX N RICHMOND ST486BATTERYDOMESTIC BATTERY SIMPLEAPARTMENTFalseTrue173317.033.016.008B1156052.01924747.002/04/2016 06:33:39 AM41.949298-87.701776(41.949297934, -87.701775686)
32477588404HS39246607/03/2010 05:45:00 PM011XX N LOCKWOOD AVE2027NARCOTICSPOSS: CRACKALLEYTrueFalse152415.037.025.0181140813.01907182.002/04/2016 06:33:39 AM41.901392-87.758226(41.901392194, -87.758225634)
32487588405HS38792307/01/2010 12:00:00 AM020XX E 75TH ST820THEFT$500 AND UNDERSTREETFalseFalse4144.08.043.061191290.01855646.002/04/2016 06:33:39 AM41.758897-87.574493(41.758896683, -87.574492818)
32497588406HS39234007/03/2010 03:05:00 PM016XX N CLYBOURN AVE820THEFT$500 AND UNDERSTREETFalseFalse181318.032.07.061170214.01911024.002/04/2016 06:33:39 AM41.911343-87.650120(41.911343228, -87.65012046)
32507588407HS38901507/01/2010 01:45:00 PM050XX W WEST END AVE2825OTHER OFFENSEHARASSMENT BY TELEPHONERESIDENCEFalseTrue153215.028.025.0261142756.01900513.002/04/2016 06:33:39 AM41.883056-87.751255(41.883055705, -87.751254884)
32517588408HS39252607/03/2010 06:30:00 PM014XX W 63RD ST2093NARCOTICSFOUND SUSPECT NARCOTICSPOLICE FACILITY/VEH PARKING LOTTrueFalse7137.016.067.0261167639.01862965.002/04/2016 06:33:39 AM41.779521-87.660962(41.779520772, -87.660962362)
32527588411HS39222907/03/2010 02:50:00 PM016XX N KEELER AVE486BATTERYDOMESTIC BATTERY SIMPLEAPARTMENTFalseTrue253425.030.023.008B1148137.01910578.002/04/2016 06:33:39 AM41.910573-87.731236(41.91057328, -87.731236005)
32537588412HS39251507/03/2010 09:00:00 AM053XX S CAMPBELL AVE1150DECEPTIVE PRACTICECREDIT CARD FRAUDRESIDENCEFalseFalse9119.014.063.0111160608.01869235.002/04/2016 06:33:39 AM41.796874-87.686566(41.796874465, -87.686566039)
32547588413HS39245507/03/2010 05:15:00 PM030XX N ODELL AVE486BATTERYDOMESTIC BATTERY SIMPLERESIDENCEFalseTrue251125.036.017.008B1126558.01919423.002/04/2016 06:33:39 AM41.935233-87.810313(41.935233424, -87.81031254)
32557588414HS39252107/03/2010 06:10:00 PM051XX W BELDEN AVE1310CRIMINAL DAMAGETO PROPERTYSTREETFalseFalse252225.031.019.0141141544.01914802.002/04/2016 06:33:39 AM41.922289-87.755352(41.922288839, -87.75535195)